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This component may be plugged into standard compile plugin of maven to compile JavaFX ( http://javafx.com/) sources. The component assumes that JavaFX SDK 1.2+ is installed on the machine were built process is run. Environment variable JFX_HOME should point to JavaFX installation directory (typically /usr/share/javafx-sdk1.2 for Linux machines). Version 0.3 is current one and is stable. Version 0.2 has deffect and compiles only basic code samples in all other cases it simply fail.

A bottom-up rewrite machine is a compiler construction tool that is often used in the compiler's back end to convert a tree-structured representation of a program into machine code -- or, in Java's case, bytecode. JBurg can also be used as a general-purpose dynamic programming engine. JBurg is descended from iburg-class BURGs, described in Fraser, Hanson, and Proebsting's paper, "Engineering a Simple, Efficient Code Generator Generator." JBurg brings similar O(N) minimum-cost tree rewriting capabilities to Java, and also allows the programmer to specify transitions between non-terminal states, that are significantly more powerful than iburg's transitive closures: JBurg transformation rules allow the transformation to inject additional program logic, which makes a JBurg specification more like a grammar than like a list of pattern-matching rules.

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

Java Prolog Interface (JPI) is an Application Provider Interface (API) for interaction between Java and Prolog programming languages. Is a bidirectional interface that communicate Java applications with Prolog program or database and Prolog procedures with Java class and methods. JPI is an abstraction layer over concrete prolog drivers over Prolog Engines. This API define all mechanism to interact with any Prolog Engine and maintain the application independent to a specific underlying engine. JPI have several connectors to open source prolog engines like SWI, YAP, XSB native engines and tuProlog, jTrolog, jLog Java based prolog engines. JPI study all related Java-Prolog integration libraries and take the betters features from each solution with the propose to achieve a common integration interface. The last feature allows switch the under laying Prolog Engine driver and the application code still be the same. JPI run over any Java Virtual Machine that support Java SE 5 or above. The project was tested over HotSpot, Open J9 and JRockit Virtual Machines over Operating Systems like Windows (7,8,10), Linux (Debian, Ubuntu) and Mac OS X. Can be deployed on Servlets Containers like Jetty, Tomcat or Glassfish Application Server. JPI can be include in any Java Project using the commonest Java Integration Development Enviroment (IDE) like Eclipse, Netbeans, IntelijIDEA and so on. JPI is developed and maintained by Prolobjectlink Project an open source initiative for build logic based applications using Prolog like fundamental Logic Programming Language in the persistence layer and application programming.

Apache Commons Crypto is a cryptographic library optimized with AES-NI (Advanced Encryption Standard New Instructions). It provides Java API for both cipher level and Java stream level. Developers can use it to implement high performance AES encryption/decryption with the minimum code and effort. Please note that Crypto doesn't implement the cryptographic algorithm such as AES directly. It wraps to OpenSSL or JCE which implement the algorithms. Features -------- 1. Cipher API for low level cryptographic operations. 2. Java stream API (CryptoInputStream/CryptoOutputStream) for high level stream encryption/decryption. 3. Both optimized with high performance AES encryption/decryption. (1400 MB/s - 1700 MB/s throughput in modern Xeon processors). 4. JNI-based implementation to achieve comparable performance to the native C/C++ version based on OpenSsl. 5. Portable across various operating systems (currently only Linux/MacOSX/Windows); Apache Commons Crypto loads the library according to your machine environment (it checks system properties, `os.name` and `os.arch`). 6. Simple usage. Add the commons-crypto-(version).jar file to your classpath. Export restrictions ------------------- This distribution includes cryptographic software. The country in which you currently reside may have restrictions on the import, possession, use, and/or re-export to another country, of encryption software. BEFORE using any encryption software, please check your country's laws, regulations and policies concerning the import, possession, or use, and re-export of encryption software, to see if this is permitted. See <http://www.wassenaar.org/> for more information. The U.S. Government Department of Commerce, Bureau of Industry and Security (BIS), has classified this software as Export Commodity Control Number (ECCN) 5D002.C.1, which includes information security software using or performing cryptographic functions with asymmetric algorithms. The form and manner of this Apache Software Foundation distribution makes it eligible for export under the License Exception ENC Technology Software Unrestricted (TSU) exception (see the BIS Export Administration Regulations, Section 740.13) for both object code and source code. The following provides more details on the included cryptographic software: * Commons Crypto use [Java Cryptography Extension](http://docs.oracle.com/javase/8/docs/technotes/guides/security/crypto/CryptoSpec.html) provided by Java * Commons Crypto link to and use [OpenSSL](https://www.openssl.org/) ciphers

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

pact-jvm-consumer-junit5 ======================== JUnit 5 support for Pact consumer tests ## Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer-junit5_2.12` * version-id = `3.5.x` ## Usage ### 1. Add the Pact consumer test extension to the test class. To write Pact consumer tests with JUnit 5, you need to add `@ExtendWith(PactConsumerTestExt)` to your test class. This replaces the `PactRunner` used for JUnit 4 tests. The rest of the test follows a similar pattern as for JUnit 4 tests. ```java @ExtendWith(PactConsumerTestExt.class) class ExampleJavaConsumerPactTest { ``` ### 2. create a method annotated with `@Pact` that returns the interactions for the test For each test (as with JUnit 4), you need to define a method annotated with the `@Pact` annotation that returns the interactions for the test. ```java @Pact(provider=&quot;test_provider&quot;, consumer=&quot;test_consumer&quot;) public RequestResponsePact createPact(PactDslWithProvider builder) { return builder .given(&quot;test state&quot;) .uponReceiving(&quot;ExampleJavaConsumerPactTest test interaction&quot;) .path(&quot;/&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(&quot;{\&quot;responsetest\&quot;: true}&quot;) .toPact(); } ``` ### 3. Link the mock server with the interactions for the test with `@PactTestFor` Then the final step is to use the `@PactTestFor` annotation to tell the Pact extension how to setup the Pact test. You can either put this annotation on the test class, or on the test method. For examples see [ArticlesTest](src/test/java/au/com/dius/pact/consumer/junit5/ArticlesTest.java) and [MultiTest](src/test/groovy/au/com/dius/pact/consumer/junit5/MultiTest.groovy). The `@PactTestFor` annotation allows you to control the mock server in the same way as the JUnit 4 `PactProviderRule`. It allows you to set the hostname to bind to (default is `localhost`) and the port (default is to use a random port). You can also set the Pact specification version to use (default is V3). ```java @ExtendWith(PactConsumerTestExt.class) @PactTestFor(providerName = &quot;ArticlesProvider&quot;, port = &quot;1234&quot;) public class ExampleJavaConsumerPactTest { ``` **NOTE on the hostname**: The mock server runs in the same JVM as the test, so the only valid values for hostname are: | hostname | result | | -------- | ------ | | `localhost` | binds to the address that localhost points to (normally the loopback adapter) | | `127.0.0.1` or `::1` | binds to the loopback adapter | | host name | binds to the default interface that the host machines DNS name resolves to | | `0.0.0.0` or `::` | binds to the all interfaces on the host machine | #### Matching the interactions by provider name If you set the `providerName` on the `@PactTestFor` annotation, then the first method with a `@Pact` annotation with the same provider name will be used. See [ArticlesTest](src/test/java/au/com/dius/pact/consumer/junit5/ArticlesTest.java) for an example. #### Matching the interactions by method name If you set the `pactMethod` on the `@PactTestFor` annotation, then the method with the provided name will be used (it still needs a `@Pact` annotation). See [MultiTest](src/test/groovy/au/com/dius/pact/consumer/junit5/MultiTest.groovy) for an example. ### Injecting the mock server into the test You can get the mock server injected into the test method by adding a `MockServer` parameter to the test method. ```java @Test void test(MockServer mockServer) { HttpResponse httpResponse = Request.Get(mockServer.getUrl() + &quot;/articles.json&quot;).execute().returnResponse(); assertThat(httpResponse.getStatusLine().getStatusCode(), is(equalTo(200))); } ``` This helps with getting the base URL of the mock server, especially when a random port is used. ## Unsupported The current implementation does not support tests with multiple providers. This will be added in a later release.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.